r Academy of Management Perspectives
2013, Vol. 27, No. 4
Online only
http://dx.doi.org/10.5465/amp.2013.0124
RESEARCH BRIEFS
GETTING TO RIGHT: HOW DO MANAGERS MAKE GOOD DECISIONS
ABOUT CUSTOMERS?
CLIVE MUIR
Stephen F. Austin State University
CHAD JACKSON
Kansas State University
RESEARCH QUESTIONS
Good customer relationships are more critical
than ever to business strategy and success. As such,
the maxim “the customer is always right” is valued
by managers who understand the benefits of acquiring and retaining quality customers. But how do
these managers get it right when it comes to making
important, tactical decisions about their customers? That was the general question that Johannes
Bauer (University of St. Gallen), Philipp Schmitt
(Goethe University Frankfurt), and their New York
University colleagues Vicki Morwitz and Russell
Winer, set out to answer in their investigation of the
decision-making behaviors of sales managers. The
team acknowledged that companies are fine-tuning
their efforts to collect and process massive amounts
of customer data aimed at increasing the lifetime
value of individual customers to their firms and
maximizing the overall asset value of their customers, yet they asserted that there is still much to learn
about how managers actually make decisions about
customers, particularly the steps involved and the
factors that shape their decision making.
Their study was guided by the concept of
bounded rationality—the idea that individuals
make decisions based on a limited amount of information about the task at hand, using their own limited mental capabilities, and according to the
environmental constraints in which the task is undertaken (Simon, 1955). Bauer and his colleagues
surmised that managers typically rely on their experience, tend to be over confident, and employ
rule-of-thumb approaches that may result in a
faster, acceptable decision. However, this may
come at the expense of decision accuracy while
foregoing optimal returns on the resources invested
in the task.
Bauer and his colleagues compared two approaches to decision making that illuminate bounded
rationality: (a) adaptive decision making, which
suggests that managers would respond differently
and flexibly to a task depending on the variables in
the environment in which the decision is being
made (Payne et al., 1993); and (b) fast and frugal
decision making, where the managers would prefer
a tested, simplified mental calculus based on a limited set of information about the issues at hand
(Gigerenzer & Goldstein, 1996).
STUDY DESIGN AND METHOD
Data were gathered from 49 sales managers working in the consumer retail sales division of a leading German bank. The firm was chosen partly
because customer relationship management had
become a strategic focus of the bank’s senior executives. Of the managers who participated in the
study, the overwhelming majority had at least four
years of experience in retail sales, and over half of
them had spent at least 10 years working in retail
sales. About two-thirds of the managers were over
30 years old, and more than half were female.
The sales managers participated in experiments
consisting of three different sets of tasks that required them to make real decisions using data about
a selected group of customers. Each task was more
complex than the preceding one, but all three tasks
used information and steps that were familiar to the
managers. Managers were divided into two groups
based on the perceived (high and low) complexity
of each task. The managers’ actions while completing the tasks were monitored using a web-based
program, MouselabWEB, which is designed to unobtrusively capture each step of the managers’ information selection and problem-solving decisions.
Which pieces of information the managers used,
the order in which the managers accessed the information, and how much time they took to arrive at a
decision were all examined by Bauer and his colleagues.
The three tasks were completed in order and
rated on their difficulty. First, managers had to
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make decisions about cross-selling: predicting
which customers would assume a new loan within
three months. The managers were able to access information about customers’ past and current loan
and account profiles, and the high-complexity task
managers were given additional information about
the customers’ children and their credit card usage
in the three months prior to the decision making.
This task was rated easy. Next, managers had to
make decisions regarding customer retention: predicting which customers would cancel their checking accounts within six months based on various
transactions in their checking and savings accounts
as well as their credit rating for previous months.
The high-complexity task managers in this task
were also given information about customers’ ages
and their prior purchase of a featured bank product.
This task was rated difficult. The final task, acquisition, involved predicting which customers would
refer new customers to the bank within 18 months.
In this case, managers were given information about
the customers’ family size, their total banking products, and the time lapse since their last transaction.
The high-complexity task groups were also able to
consider the customers’ number of children as well
as account transactions and number of remittances.
This task was rated very difficult.
After collecting and analyzing the data, Bauer
and his colleagues had to determine the decision
strategies that managers used based on the changes
that occurred as the managers moved from task to
task as well as how quickly and what amount of
available information managers used in completing
each task.
KEY FINDINGS
Bauer and his colleagues found that each of the
four decision-making strategies investigated performed well, that adaptive decision making does
not negatively impact the quality of decisions, and
that fast and frugal heuristics can lead to increases
in decision accuracy—which is contrary to previous research (Hutchinson, Alba, & Eisentein, 2010).
The majority of managers in the study were adaptive in their decision-making processes, but there
were several instances where managers made accurate decisions quickly and on the basis of very limited information. Bauer and his colleagues
suggested that the fast and frugal heuristics strategy
leads to a significant increase in accuracy due to the
manager’s learned ability to focus on the most important pieces of information and ignoring irrelevant pieces of information.
These results indicate that experienced managers
have learned to ignore extraneous information and
November
focus on only the most important pieces of information when making customer-management decisions. Moreover, experienced managers may use
simple decision-making strategies with good results while reducing cognitive effort without sacrificing the quality of the decision.
CONCLUSIONS AND IMPLICATIONS
As businesses are presented with increasing
amounts of information, understanding the decisionmaking process is more critical to managers than
ever. In that regard, recent studies from a variety of
related disciplines have shed light on managerial
decision making as a communicative process
(Baraldi, 2013): as a way to evaluate entrepreneurial opportunities (Wood & Williams, 2013), as a tool
for determining which technology to adopt (Spencer,
Buhalis, & Moital, 2013), and as a mechanism for
evaluating the impact of social ties and human capital on the choices made by small business owners (Jansen, Curseu, Vermeulen, Geurts, & Gibcus,
2013).
Understanding managerial decision making is
important not only for improving customer relationship management but for responding to a wide
variety of managerial challenges. This study highlights several key factors to consider if managers
are to improve their customer-management decision making. First, the study indicates that being
overconfident can negatively affect decision quality. Bauer and his colleagues suggested that identifying and managing overconfidence is a promising
strategy for increasing the quality of decisions made
by managers. One approach for managing overconfidence is the use of timely and precise feedback
(Russo & Shoemaker, 1992). Another way to curb
overconfidence is to remind managers to consider
both supporting and contradictory reasons for making a decision (Koriat, Lichtenstein, & Fischhoff,
1980).
Second, a common challenge for decision makers
is dealing with the overwhelming abundance of
data that is often available. The results of this study
indicate that leaders should support their managers
by helping them to focus on only the most important pieces of information when making a decision.
Managers in turn must learn to trust their intuition
about which pieces of information are most important and useful.
Finally, while this study focused on decision
making in customer-management scenarios, Bauer
and his colleagues suggested that their findings
may be applicable to a variety of management
decision-making settings. While further research
should be done in different management decision
Muir and Jackson
2013
contexts to validate this claim, Bauer and his colleagues provided an intriguing anecdote by relating
the results of this study to the decision-making
practices of venture capital professionals who seem
to prefer to reduce the amount of extraneous information when making investment decisions. As organizations collect more and more data about their
customers, transactions, and competitors, and as
more emphasis is put on the growing field of data
mining and business analytics, this study seems to
indicate that more information isn’t always better
for decision making, and managers need to be judicious in their selection and use of information
when making decisions.
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